提出新方法提升光场显微镜三维重建精度与物理一致性。
From Pixels to Views: Learning Angular-Aware and Physics-Consistent Representations for Light Field Microscopy
- 自监督学习预测被遮挡视角，捕捉角度结构先验
- 引入可微渲染损失，确保重建结果与点扩散函数一致
- 构建大规模数据集，推动光场显微重建研究
光场显微镜（LFM）已成为神经科学中大规模活体神经成像的重要工具，具有单次曝光、大视场和高时间分辨率等优势。然而，基于学习的三维重建在扩展光场显微镜（XLFM）中仍不成熟，主要受限于缺乏标准化数据集以及难以高效建模其角度-空间结构且保持物理一致性。本文提出三项关键贡献：首先，构建了XLFM-Zebrafish基准数据集与评估套件；其次，提出掩码视角建模（MVN-LF），通过预测被遮挡视角实现自监督学习，提升数据效率；第三，设计光学渲染一致性损失（ORC Loss），一种可微渲染约束，强制重建体积与其基于点扩散函数的前向投影对齐。在XLFM-Zebrafish基准上，本方法相比现有最优基线提升PSNR达7.7%。
原文摘要 · Abstract (English)
Light field microscopy (LFM) has become an emerging tool in neuroscience for large-scale neural imaging in vivo, notable for its single-exposure volumetric imaging, broad field of view, and high temporal resolution. However, learning-based 3D reconstruction in XLFM remains underdeveloped due to two core challenges: the absence of standardized datasets and the lack of methods that can efficiently model its angular-spatial structure while remaining physically grounded. We address these challenges by introducing three key contributions. First, we construct the XLFM-Zebrafish benchmark, a large-scale dataset and evaluation suite for XLFM reconstruction. Second, we propose Masked View Modeling for Light Fields (MVN-LF), a self-supervised task that learns angular priors by predicting occluded views, improving data efficiency. Third, we formulate the Optical Rendering Consistency Loss (ORC Loss), a differentiable rendering constraint that enforces alignment between predicted volumes and their PSF-based forward projections. On the XLFM-Zebrafish benchmark, our method improves PSNR by 7.7% over state-of-the-art baselines.
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